New York, United States – 4th May 2026 – SardineAI Corp today released a comprehensive industry report that reframes e-commerce returns fraud prevention as a user-lifecycle issue and recommends operational changes and signal integration to detect abusive return behavior earlier in the customer journey.
The report describes a shifting boundary for returns fraud, arguing that the moment a refund request arrives is often too late to determine whether an incident reflects routine customer behavior or systematic abuse. The research draws on industry data and operational case studies to show that suspicious activity frequently appears in purchase history, device behavior, support interactions, linked identities, and broader abuse patterns well before a return is submitted.
The document presents a set of practical approaches intended to help merchants and marketplaces move from isolated return-policy enforcement to lifecycle-oriented detection and decisioning. Within this framework, e-commerce returns fraud prevention is positioned as a continuous process that begins well before a return request is initiated and extends across the full customer journey.
The report cites widely used industry estimates that 7 to 10 percent of returns are fraudulent and notes an annual cost to U.S. retailers in the range of $18 billion. Those figures are used to illustrate the scale of loss created by a mix of opportunistic and organized actors who exploit flexible return policies. The research emphasizes that effective e-commerce returns fraud prevention requires recognizing diverse abuse patterns rather than treating all incidents as a single category.
The research emphasizes that returns abuse takes many forms, including wardrobing, concession abuse, empty-box claims, bricking, receipt fraud, and purchases made with compromised payment instruments that are later refunded to fraudster-controlled destinations. Each pattern imposes distinct investigative and operational requirements, and the report underlines that treating returns fraud as a single, homogeneous problem leads to persistent blind spots.
Special attention is paid to marketplace environments, where trust between multiple parties and the sheer volume of transactions create structural exposure. The report outlines why marketplaces often lack the depth of linked signals necessary to identify a dedicated returns abuser early. Without cross-account visibility and device-based linkage, abuse can remain distributed across accounts and sellers until multiple transactions and interactions have already imposed financial, logistical, and reputational costs across the platform.
A central finding is that the difficulty in addressing returns abuse stems not from a lack of rules but from a lack of context. Static rules triggered by individual events struggle to surface patterns that only become visible when the return is interpreted alongside prior behavior, shared device fingerprints, support contact history, and linked identities. The report stresses that stronger returns fraud programs are those that evaluate whether a return request aligns with the broader account story, including unusual return frequency, inconsistent device signals, or support interaction patterns that correlate with concession-driven fraud.
The report also documents how legacy tools create lifecycle blind spots. Systems focused narrowly on account opening or payment-stage checks were not designed to capture post-purchase dynamics such as pattern-based wardrobe-style returns or repeated concessions. Fraud and support stacks built independently for different operational goals leave gaps between onboarding, transaction monitoring, support resolution, and returns adjudication. The document recommends explicit integration points and data-sharing practices to reduce those gaps without imposing blanket customer friction.
Another section examines how technological shifts change the threat landscape. The report flags generative AI as a factor that can increase scale and plausibility of fraudulent artifacts, including fabricated receipts and more convincing support interactions. That trend elevates the importance of behavioral and device-driven analysis as complementary signals. The report recommends combining device fingerprinting, behavioral biometrics fraud detection, linked-account analysis, and account history to distinguish legitimate customer questions from coordinated or automated abuse attempts that appear natural on the surface.
The document outlines specific categories of returns abuse that merit ongoing attention. Wardrobing remains a persistent issue in apparel categories where use-before-return behavior mimics legitimate returns. Concession abuse exploits support processes and economic thresholds that make refunds or credits administratively cheaper than returns handling. Empty-box claims and bricking rely on condition misrepresentation, while receipt fraud and controlled refund channels are common in organized theft rings. The report explains that these patterns require different investigative responses and that early detection depends on connecting disparate signals rather than escalating every case.
Policy design receives careful treatment in the report. The study argues that harsher, blanket restrictions on returns risk damaging customer trust and reducing conversion, and that the objective should instead be better decisions informed by broader context. The recommended approach seeks to preserve generous return policies for bona fide customers while enabling targeted scrutiny where account history, device patterns, and linked identities indicate disproportionate risk. The report provides examples of decision frameworks that privilege context over singular event triggers and that aim to reduce loss without imposing unnecessary customer friction.
Finally, the report calls for investment in cross-account visibility, lifecycle monitoring, and signal interoperability across fraud, support, and payments teams. It presents operational considerations for merchants and marketplaces that wish to move from reactive refund adjudication to proactive lifecycle detection, and it highlights the potential to recover margin and trust by identifying abuse earlier in the user journey.
About SardineAI Corp
SardineAI Corp is a company that researches and develops fraud detection technologies and operational guidance for online businesses. The company produces reports, technical guidance, and analytical tools intended to help merchants and marketplaces mitigate financial loss while maintaining customer experience. SardineAI Corp works with clients across retail and platform environments to translate lifecycle data into fraud decisioning strategies.
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